On the Partition Function and Random Maximum A-Posteriori Perturbations

Understand

In this paper we relate the partition function to the max-statistics of random variables.

• In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models.
• As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the corresponding partition function.
• We show that our method excels in the typical "high signal - high coupling" regime that results in ragged energy landscapes difficult for alternative approaches.